Hi,
the Likert scale seems to be an approximation of a continuous variable - so AFAIK ordinal regression would be better though it takes more samples.
http://stats.stackexchange.com/quest...gression-model
regards
Hi,
If I have a variable called time spend following politics with the following possible responses:
No time
under half an hour a day
between half an hour and one hour
between one and one and a half hours
between one and a half and two hours
over two hours
I want to look at predictors of this model (all nominal variables). I am not sure whether a ordinal or multinomial regression works best for this. Could someone please advise.
Thanks
Hi,
the Likert scale seems to be an approximation of a continuous variable - so AFAIK ordinal regression would be better though it takes more samples.
http://stats.stackexchange.com/quest...gression-model
regards
There is a formal test for the legitimacy of using ordered logistic regression - SAS calls this the proportional odds assumption. If you reject the null then you use multinomial rather than ordered regression. However, with large samples and many IV you can reject the null when you should not. Allison suggest you need at least ten cases for each level of the DV [here 60] but I am guessing you have that many cases
"Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995
hi noetsi,
maybe it would make sense to run the model and see how well it fits, first? If it does not, then probably the proportional odds assumption is not verified.
regards
I don't think fit has anything to do with the proportional assumption. Its an assumption that you are testing, I forget which one, not how well the model fits the data. In theory, if commonly not in fact, you test model assumptions then look at the results.
Of course in reality if the results are no good who would waste time on the test of assumptions
"Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995
Thanks for the helpful reply. Yes I have heard about proportional odds and how stringent the test is. My understanding was that you could make a dummy for each of the possible outcomes and run normal logit models, then if the odds ratios for the predictors were different across the models (6 in my case) then you could be more sure that the proportional odds assumption has actually been violated. Any thoughts on this?
Also, if possible, would it be possible to tell me how my interpretation of the model would change. For example, if I am comparing high political interest to a low political interest (IV) against each of the different time periods for following politics of TV (comparison group = watching no TV), is it possible to say that if the odds ratio for watching 31-60 mins is larger than watching 1-30 mins in comparison to watching no TV then there is a greater likelihood of watching 31-60 mins? Or am I just limited to saying that if the odds ratios are all over 1 then there is an increased likelihood for all lengths of TV usage in comparison to no TV.
Last edited by DGM1; 03-28-2016 at 06:56 PM.
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